Update app.py

app.py

@@ -6,71 +6,59 @@ from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
 import pickle
-# from sentence_transformers import SentenceTransformer
 from langchain import HuggingFaceHub
 from langchain.chains.question_answering import load_qa_chain
-
 import os
 
-# model = SentenceTransformer('all-MiniLM-L6-v2')
-
-with st.sidebar:
-    st.title('LLM PDF Chats')
-    st.markdown('''
-                ## about
-                - This is LLM power chatbot
-                - By [Prathamesh Shete]('https://www.linkedin.com/in/prathameshshete')
-                
-                
-                ''')
-    add_vertical_space(5)
-    st.write('Made By Prathamesh')
-    
-load_dotenv()  
-def main():
+def main(pdf):
     st.header('Chat With PDF')
-    
-    pdf = st.file_uploader('Upload Your PDF', type='pdf')
 
-if pdf is not None:
-    pdf_reader = PdfReader(pdf)
-    
-    text = ''
-    for page in pdf_reader.pages:
-        text += page.extract_text()
-    
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=1000,
-        chunk_overlap=200,
-        length_function=len
-    )
-    
-    chunks = text_splitter.split_text(text=text)
-    
-    store_name = pdf.name[:-4]
-    
-    if os.path.exists(f'{store_name}.pkl'):
-        with open(f'{store_name}.pkl', 'rb') as f:
-            VectorStore = pickle.load(f)
-    else:
-        embeddings = HuggingFaceEmbeddings()
-        VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
-        with open(f'{store_name}.pkl', 'wb') as f:
-            pickle.dump(VectorStore, f)
-    
-    ask_query = st.text_input('Ask question about PDF: ')
-    
-    if ask_query:
-        docs = VectorStore.similarity_search(query=ask_query, k=3)
-        llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0, "max_length": 64})
-        chain = load_qa_chain(llm=llm, chain_type='stuff')
-        response = chain.run(input_documents=docs, question=ask_query)
-        st.write(response)
+    if pdf is not None:
+        pdf_reader = PdfReader(pdf)
+
+        text = ''
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1000,
+            chunk_overlap=200,
+            length_function=len
+        )
+
+        chunks = text_splitter.split_text(text=text)
+
+        store_name = pdf.name[:-4]
+
+        if os.path.exists(f'{store_name}.pkl'):
+            with open(f'{store_name}.pkl', 'rb') as f:
+                VectorStore = pickle.load(f)
+        else:
+            embeddings = HuggingFaceEmbeddings()
+            VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
+            with open(f'{store_name}.pkl', 'wb') as f:
+                pickle.dump(VectorStore, f)
+
+        ask_query = st.text_input('Ask question about PDF: ')
+
+        if ask_query:
+            docs = VectorStore.similarity_search(query=ask_query, k=3)
+            llm = HuggingFaceHub(repo_id="google/flan-t5-xl", model_kwargs={"temperature": 0, "max_length": 64})
+            chain = load_qa_chain(llm=llm, chain_type='stuff')
+            response = chain.run(input_documents=docs, question=ask_query)
+            st.write(response)
 
-    
-    
-    
-    
-    
 if __name__ == "__main__":
-    main()
+    load_dotenv()
+
+    st.sidebar.title('LLM PDF Chats')
+    st.sidebar.markdown('''
+        ## About
+        - This is LLM power chatbot
+        - By [Prathamesh Shete]('https://www.linkedin.com/in/prathameshshete')
+    ''')
+    add_vertical_space(5)
+    st.sidebar.write('Made By Prathamesh')
+
+    pdf = st.file_uploader('Upload Your PDF', type='pdf')
+    main(pdf)